Laboratoire National de Métrologie et d'Essai, 1 rue Gaston Boissier, 75724 Paris Cedex 15, France.
Environ Sci Process Impacts. 2013 Sep;15(9):1692-705. doi: 10.1039/c3em00168g.
The objective of this paper was to demonstrate how multivariate statistics combined with the analysis of variance could support decision-making during the process of redesigning a water quality monitoring network with highly heterogeneous datasets in terms of time and space. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were selected to optimise the selection of water quality parameters to be monitored as well as the number and location of monitoring stations. Sampling frequency was specifically investigated through the analysis of variance. The data used were obtained between 2007 and 2010 at the Long-term Environmental Research Monitoring and Testing System (OPE) located in the north-eastern part of France in relation with a geological disposal of radioactive waste project. PCA results showed that no substantial reduction among the parameters was possible as strong correlation only exists between electrical conductivity, calcium or bicarbonates. HCA results were geospatially represented for each field campaign and compared to one another in terms of similarities and differences allowing us to group the monitoring stations into 12 categories. This approach enabled us to take into account not only the spatial variability of water quality but also its temporal variability. Finally, the analysis of variances showed that three very different behaviours occurred: parameters with high temporal variability and low spatial variability (e.g. suspended matter), parameters with high spatial variability and average temporal variability (e.g. calcium) and finally parameters with both high temporal and spatial variability (e.g. nitrate).
本文旨在展示多元统计分析与方差分析相结合如何在重新设计水质监测网络时提供决策支持，该网络的数据在时间和空间上具有高度异质性。主成分分析（PCA）和层次聚类分析（HCA）被选来优化水质参数的选择以及监测站的数量和位置。通过方差分析特别研究了采样频率。所使用的数据是在法国东北部的长期环境研究监测和测试系统（OPE）于 2007 年至 2010 年间获得的，与放射性废物地质处置项目有关。PCA 结果表明，由于电导率、钙或碳酸氢盐之间仅存在强相关性，因此参数之间不可能有实质性的减少。HCA 结果在每个野外考察期间进行了地理空间表示，并根据相似性和差异性进行了比较，使我们能够将监测站分为 12 类。这种方法不仅考虑了水质的空间变异性，还考虑了其时间变异性。最后，方差分析表明存在三种非常不同的行为：时间变异性高而空间变异性低的参数（例如悬浮物）、空间变异性高而时间变异性平均的参数（例如钙），以及同时具有高时间和空间变异性的参数（例如硝酸盐）。
